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Abstract Fishing has the potential to influence the life‐history traits of exploited populations. However, our understanding of how fisheries can induce evolutionary genetic changes remains incomplete. The discovery of large‐effect loci linked with ecologically important life‐history traits, such as age at maturity in Atlantic salmon (Salmo salar), provides an opportunity to study the impacts of temporally varying fishing pressures on these traits. A 93‐year archive of fish scales from wild Atlantic salmon catches from the northern Baltic Sea region allowed us to monitor variation in adaptive genetic diversity linked with age at maturity of wild Atlantic salmon populations. The dataset consisted of samples from both commercial and recreational fisheries that target salmon on their spawning migration. Using a genotyping‐by‐sequencing approach (GT‐seq), we discovered strong within‐season allele frequency changes at the vgll3 locus linked with Atlantic salmon age at maturity: fishing in the early season preferentially targeted the vgll3 variant linked with older maturation. We also found within‐season temporal variation in catch proportions of different wild Atlantic salmon subpopulations. Therefore, selective pressures of harvesting may vary depending on the seasonal timing of fishing, which has the potential to cause evolutionary changes in key life‐history traits and their diversity. This knowledge can be used to guide fisheries management to reduce the effects of fishing practices on salmon life‐history diversity. Thus, this study provides a tangible example of using genomic approaches to infer, monitor and help mitigate human impacts on adaptively important genetic variation in nature.

Background Due to digitization in the medical sector, many healthcare interactions are switched to online services. This study assessed the acceptance of video consultations (VCs) in cancer care, and determined drivers and barriers of acceptance. Methods A cross-sectional online-based survey study was conducted in Germany from February 2022 to February 2023. Recruitment took place at oncology outpatient clinics, general practitioners, oncology practices and via cancer-related social media channels. Inclusion criteria were a cancer diagnosis, cancer treatment and internet access. Sociodemographic, medical data, eHealth-related data were acquired via an online assessment. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used to determine the acceptance of VC and its predictors. Results Of N = 350 cancer patients, 56.0% ( n = 196) reported high acceptance of VC, 28.0% ( n = 98) stated moderate acceptance and 16.0% ( n = 56) indicated low acceptance. Factors influencing acceptance were younger age (β = –.28, p